jrte, accommodating individual differences in the design

Journal of Research on Technology in Education ��7Copyright © 2007, ISTE (International Society for Technology in Education), 800.336.5191(U.S. & Canada) or 541.302.3777 (Int’l), [email protected], www.iste.org. All rights reserved.

AccommodatingIndividualDifferencesintheDesignofOnlineLearning

Environments:AComparativeStudyMahnazMoallem

University of North Carolina, Wilmington

AbstractThe purpose of this paper is to report the results of a comparative and descriptive study that examined the relationship and effects of incorporating students’ learning styles in the design of instruction and the outcome of students’ learning, including their attitude and satisfaction. The paper will first explain how the literature on learning styles was used to develop a list of assumptions about learning styles, and further how these assumptions were used to identify a learning style model. It will also provide a detailed description of the process of using the learning style model to design and develop multiple instructional materials for two units of instruction for an online course. Finally, the paper will report the effects of this approach on students’ learning and their perception, attitude and satisfaction in comparison with instruc-tional materials that are designed and developed on the basis of the content and objectives, without incorporating students’ different learning styles. (Keywords: learning styles and online learning, instructional design and development, distance education.)

Experienced educators have long supported the notion that individual differ-ences play an important role in learning and instruction. They agree that learn-ers filter instruction through a set of individual lenses (Jonassen & Grabowski, 1993) and tend to manipulate perceived information in different ways, achieve understanding at different rates and in various learning contexts (Barbe & Milone, 1981; Corno & Snow, 1986; Felder, 1993; Felder & Silverman, 1988; Pask, 1988). Experimental studies have also confirmed educators’ beliefs by showing how students’ styles of learning and thinking make a difference in their academic achievement (e.g., Kim & Michael, 1995; Saracho, 1993; Zhang, 2002). This research suggests that learners whose learning styles match with the given teaching or instructional style tend to retain information longer, ap-ply it more effectively, and retain more positive attitudes toward the subject of the course than those who experienced clashes in teaching/learning styles (e.g., Dunn, 1995, 1999; Felder, 1993; Felder, Felder, & Dietz, 2002; Lovelace, 2005; Riding & Grimly, 1999). However, not everyone agrees with matching learning styles and teaching styles (e.g., Jonassen, 1988; Rector & Henderson, 1970). Rector and Henderson (1970) have determined through their research that the effect of various teaching strategies depends on such factors as the nature of the concept to be taught, the students’ characteristics, and the time available. In their study, no significant difference was found in different teach-ing strategies and student achievement.

The concept of individual differences presents a profound challenge for in-structional designers and educators, as research seems to suggest that the quality

JRTE, 40(2), 217–245

��8 Winter�007-�008:Volume 40 Number 2Copyright © 2007, ISTE (International Society for Technology in Education), 800.336.5191

(U.S. & Canada) or 541.302.3777 (Int’l), [email protected], www.iste.org. All rights reserved.

of learning material is enhanced if the material is designed to take into account learners’ individual learning styles (e.g., De Vita, 2001; Rasmussen, 1998; Rid-ing & Grimley, 1999). When designing instruction for a universal audience and for an environment that can be dominated easily by text-based communication and heavy reliance on independent learning skills (e.g., in online or Web-based courses), the designer’s/teacher’s challenge is to produce a course or instruc-tional material that does not have an obvious tilt toward one learning and thinking style and is diversified enough to meet multiple learning styles. Thus, it is imperative that teachers/instructional designers consult research on learn-ing styles as they devise their adaptive learning materials (Jonassen & Wang, 1993; McLoughlin, 1999; Rowntree, 1992). However, even though current research literature in the area of learning styles and strategies provides teach-ers/instructional designers with insights into individual differences in learning and performance that can be factored into the design process, little research has focused on the relationship between instructional design of learning materials and learning styles.

The purpose of this study was to examine (i) how and in what ways students’ learning styles can be incorporated in the design of instruction, and (ii) what the effects of such an approach have on students’ learning and their attitude and satisfaction. The study attempted to answer the following questions: 1) Given the controversial literature on learning styles, can one establish a list of assumptions about learning styles? 2) Which learning style model can be used to design instruction? 3) What are some instructional design specifications for integrating students’ learning styles that can guide design and development of instruction in an online learning environment?, and (4) What are the effects of these design specifications on students’ learning, their perception, attitude and satisfaction in comparison with instruction that is designed and developed us-ing content and objectives and without incorporating student different learning styles?

LEARnInGSTYLESRESEARCH:ESTABLISHInGALISTOFASSUMPTIOnS

Review of research on learning style theory does not point to a list of con-clusive results. For instance, while some studies show that there may be quali-tative changes in the learning style of an individual over time (e.g., Cornett, 1983; Pinto, Geiger, & Boyle, 1994; Price, 1980), others suggest that one’s learning style is stable (e.g., Claxton & Ralston, 1978; Cornett, 1983; Kolb, 1976). Moreover, for each research study supporting the principle of matching instructional style and learning style (e.g., Claxton & Murrell, 1987; Canino & Cicchelli, 1988; Ford & Chen, 2001; Hudak, 1985;Lovelace, 2005; Sch-meck, 1988; Watkin, 1978), there seems to be a study rejecting the matching hypothesis (Dunn, Beaudry, & Klavas, 1989; Honey & Mumford, 1982, 1986; Kolb, 1985). Hence, it seems that there still remains much discussion about the nature of the construct of learning style, and whether it is more effective to match or mismatch learning style with instructional style. Despite the above controversies in the literature and a variety of learning style approaches, we may


develop a list of assumptions underlying the concept of learning styles on the basis of existing evidence. These assumptions that are less debatable can then be used as guidelines for designing adaptive, flexible instruction.

• Learners exhibit different approaches to acquisition of knowledge (e.g., Ka-gan, 1976; Grigorenko & Sternberg, 1995; Sternberg, 1998; Watkin, 1978; Zhang, 1999, Zhang & Sternberg, 1998).

• Learning style may be assessed using a questionnaire or psychometric test (e.g., Biggs, 1992; Gregorc, 1985; Myers, 1980; Myers & McCaulley, 1988; Sternberg, 1997) even though the test may be, primarily, a measure of time required to learn (Berliner, 1979; Carroll, 1989).

• Alternative theories of styles use a common root word (“style”) and cover roughly similar attributes, but with a different label (Sternberg, 1998).

• Learners may alter their learning styles, depending on the task (e.g., Biggs, 1979; Entwistle, 1981; Sternberg, 1998; Sternberg & Grigorenko, 1995).

• By relating research on learning styles to the design of the learning environ-ment, it is possible to study how learners approach their learning (Biggs, 1987; Entwistle & Ramsden, 1983).

• It is possible and desirable to adapt the instruction to accommodate differ-ences in styles or preferences (e.g., Canino & Cicchelli, 1988; Hudak & An-derson, 1984; Lovelace, 2005; Witkin, Moore, Goodenough, & Cox, 1977).

SelectingaLearningStyleModel:Felder-SilvermanDimensionsofLearningStyles

A number of learning style models have been proposed over the last three decades (e.g., Hill’s Cognitive Style Mapping, 1976; Kolb’s Learning Styles, 1984; Dunn & Dunn Learning Styles, 1978; Grasha-Reichmann Learning Style Scales, 1996 (Grasha, 1996); Gregorc, Learning Styles, 1985; Hermann Brain Dominance Models, 1996; Felder-Silverman, Learning Model, 1988; Howard Gardener’s Multiple Intelligence Theory, 1983), and each has been used in con-structing courses in classes ranging from K to 16.Given the above-mentioned list of assumptions derived from the literature, the Felder-Silverman Dimen-sions of Learning Style model (1988) seems to be the most appropriate model for learning styles.Felder and Silverman developed their learning style model based on a composite of several theories (e.g., Jung’s theory of psychological types, information processing). The model combines several dimensions pre-sented in the Myers-Briggs model (Sensing/Intuitive) with Kolb’s information processing dimension (Active/Reflective). It also avoids the complexity of the Dunn and Dunn model. The core idea of Felder and Silverman model is that we, as instructors, should not teach each student exclusively according to his or her preferences, but rather to strive for a balance of instructional methods. Moreover, the teaching style with which students feel most comfortable may not correspond to the style that enables them to learn most effectively (Felder & Henriques, 1995). Felder and Silverman (1988) classify students’ learning styles according to five questions (see Table 1, p. 220).

Given these five questions, Felder-Silverman (1988), and later Soloman and Felder (2002), developed the Index of Learning Style (ILS). The ILS is a 44



question, self-scoring instrument, which assesses preferences on four dimensions of learning (the fifth dimension, inductive/deductive was removed from the in-dex later) (active/reflective, sensing/intuitive, visual/verbal, sequential/global).

AConceptualFrameworkforIntegratingLearningStyleModelintotheDesignandDevelopmentofInstruction

The Felder and Soloman’s Index of learning style model was used to identify specifications for integrating learning style theory into the design of the instruc-tional materials. Given the model, three main characteristics were defined for developing instructional materials that are tailored to students’ multiple learn-ing styles (see Figure 1). First, instructional materials should increase self-aware-ness and meta-cognition (Apter, 2001; Sadler-Smith, 2001). Knowledge of learning styles can be used to increase the self-awareness of students about their strengths and weaknesses as learners. Research on learning styles and achieve-ment has shown that teaching students how to learn and how to monitor and manage their own learning styles is crucial to their academic success (Atkinson, 1998; Biggs & Moore, 1993; Matthews, 1991). Second, the materials should balance learning tasks and activities so that they would accommodate all learn-ers by taking into account four dimensions of the model (Baldwin, & Sabry, 2003; McLoughlin, 1999). Third, while students should be able to choose to

Questions StudentLearning

• What type of information does the student preferentially perceive?

• Sensing learners—concrete, practical, ori-ented toward facts and procedures

• Intuitive learners—conceptual, innovative, oriented toward theories and meanings

• Through what sensory mo-dality is sensory information most effectively perceived?

• Visual learners—prefer visual representations of presented material—pictures, diagrams, flow charts

• Verbal learners—prefer written and spoken explanations

• With which organization of information is the student most comfortable?

• Inductive learners—prefer presentations that proceed from the specific to the general

• Deductive learners—prefer presentations that go from the general to the specific

• How does the student prefer to process information?

• Active learners—learn by trying things out, working with others

• Reflective learners—learn by thinking things through, working alone

• How does the student prog-ress toward understanding?

• Sequential learners—linear, orderly, learn in small incremental steps

• Global learners—holistic, system thinkers, learn in large leaps

Table1:Students’LearningStylesAccordingtoFiveQuestionsAskedbyFelder-Silverman(1988)

Journal of Research on Technology in Education ��Copyright © 2007, ISTE (International Society for Technology in Education), 800.336.5191(U.S. & Canada) or 541.302.3777 (Int’l), [email protected], www.iste.org. All rights reserved.

learn in a manner they prefer, they should also be challenged to learn in a less preferred manner (Felder & Soloman, 1998), which would provide practice and feedback in ways of thinking and solving problems with which they may not initially be comfortable.

Given the above general framework, a list of flexible and adaptive design specifications (content, curriculum sequence, presentation, navigation/selection, meta-cognitive approach) was identified. According to these specifications, the instructional materials and strategies are complementary, designed to accom-modate all learners by providing multiple instructional opportunities (see Table 2), while allowing learners to traverse the course materials according to their learning styles. The specifications listed in Table 2 (p. 222–223) were then used to design instructional materials for two units of instruction that were catered to learning needs of individuals.

In order to determine the proper organization for the multiple instructional opportunities in the design of the materials for each unit, Kolb’s theory of expe-riential learning (Kolb, 1984) was used as a framework. The core of the Kolb’s theory is that learners progress through a four stage learning cycle in which concrete experience leads to observation and reflection, which then leads to abstract concept formation (Kolb, Boyatzis, & Mainemeli, 2000). The development of concepts, in turn, leads to new experiences and further experimentation, in cyclic fashion. It is argued that the learning cycle can begin at any one of the four points—and that it should be approached as a continuous spiral. Thus, effective learners tend to proceed through all four stages, but they continually choose

Figure 1: The balanced model for designing instructional materials

�� Winter�007-�008:Volume 40 Number 2Copyright © 2007, ISTE (International Society for Technology in Education), 800.336.5191


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which set of learning abilities they will use in a specific learning situation (see Figure 2). Kolb and Fry (1975) argue that the learning cycle can begin at any one of the four points—and that it should be approached as a continuous spi-ral. For example, in grasping experience some learners perceive new information through experiencing the concrete, tangible, felt qualities of the world, relying on their senses and immersing themselves in concrete reality. Others tend to perceive, grasp, or take hold of new information through symbolic representa-tion or abstract conceptualization—thinking about, analyzing, or systematically planning, rather than using sensation as a guide. Similarly, in transforming or processing experience, some learners are careful and tend to carefully watch oth-ers who are involved in the experience and reflect on what happens, while oth-ers choose to jump right in and start doing things. The watchers favor reflective observation, while the doers favor active experimentation. Thus, while learners may choose to proceed with learning the content differently, instructional mate-rials should provide opportunity for learners to experience all four stages.

Using the Kolb theory as a framework, the unit contents and instructional materials were organized into four different levels that correspond to the four stages of learner process. Applying the four stages of the learning cycle led to organizing multiple instructional activities (collaborative real-world activity, reflective individual task, large and small group discussion, self-assessment in-strument) in a way to enable students to experience all four stages, while choos-ing to proceed with their own preferred style. In other words, active learners were able to build on the concrete experience by engaging in a collaborative real-world learning activity (preferred learning style) before reflecting on present and prior learning through a reflective individual task (less preferred learning

Figure 2: Experiential learning cycle and basic learning styles (Kolb, 1984)


style). As a result of engaging in a real-world task, active learners were able to form abstract concepts from experience (self-assessment instrument) and apply recently learned content to new tasks and situations by completing a product. The idea was to organize multiple learning materials in a way that learners have an opportunity to choose to learn in a manner they prefer while they are also challenged to learn in a less preferred manner. Kolb’s learning cycle assured achievement of the unit learning outcomes, while incorporating multiple learn-ing styles.

Multiple instructional materials were grouped by unit content and outcome goals. The content of each unit consisted of: 1) unit overview, 2) learning objec-tives, 3) required and optional reading materials, 4) lecture notes in multiple format, 5) collaborative learning activity with an expected product, 6) reflective individual journal, 7) large and small group asynchronous discussion, 8) real time/synchronous discussion and demonstration, and 9) self-assessment instru-ment/quiz. Collaborative learning activities were designed with a strong focus on meta-cognition and problem solving. The collaborative learning activity confronted students with a real-world situation and context that was problem-atic and in need of development of a solution or a product. Through inquiry into how peers might approach tasks and by comparing peers’ view(s) with their own, students reflected on aspects of the situation that required change. Reflec-tion and self-evaluation then led to team discussion and consensus on a specific plan of action, which in turn resulted in creating the team product. A reflective, individual assignment was also provided in which students were encouraged to reflect on their observations. Self-assessment instruments offered students the option of managing and controlling their own learning and making abstract conceptualizations.

METHODOLOGYThe study aimed to explore the effects of incorporating students’ learning

styles in the design of instruction on student learning, expectations, attitude, and satisfaction. The exploratory nature of the study suggested a qualitative methodology, although quantitative data were collected and analyzed in order to explore possible effects of incorporating students’ learning styles in the design of instruction, in general, and instructional materials and strategies, in particu-lar. As such, rather than just obtaining statistically significant results a compara-tive design was used as a basis for further exploration (Yin, 1994).

TheCourseandItsParticipantsThe proposed conceptual framework used was to design and develop in-

structional materials and strategies for two units of instruction for a three unit graduate level online course in the area of instructional design theories and research in a southeastern university. The course is a foundation course required of all students enrolled in the Instructional Technology program. The majority of students take the course at the start of their program of study. WebCT Vista was used to deliver the course and its instructional materials. Horizon Wimba was used as a Synchronous Learning Management System (SLMS) for conduct-ing real time discussion and presentations.



The participants consisted of 14 graduate students (ten female and four male), all enrolled in an instructional technology master’s program. The researcher was also the instructor of record for the course. Students were assured of anonym-ity and were informed of their ability to choose not to participate at any time. Thus, none of the students was mandated to participate and all students agreed to participate in the project. Students’ ages ranged from 26 to 60 years old. They represented a heterogeneous group of students with regard to age, back-ground, and experience. All students participated in instruction of two units (each unit was one-week long) that were designed, and developed and taught using goals and objectives—a traditional design approach that does not con-sider students’ learning styles. The same group of students then participated in instruction of two other units of instruction that were designed, developed, and taught using the learning styles model described earlier (experimental design). The content and complexity of the units for both approaches were essentially comparable.

InstrumentationSeveral instruments were used during this investigation. An autobiographical

narrative provided demographic and background knowledge about students. Felder and Soloman’s index of learning styles instrument was used to assess students’ learning styles. A survey was also used to assess students’ perception of the course materials and instructional strategies utilized to deliver the instruc-tion and their attitude and satisfaction. Students’ knowledge and understanding of the materials (units’ learning outcomes) were assessed through the analysis of the products developed during each unit (students’ reflective journals/blogs and collaborative team products) and students’ postings in the discussion forum. Students’ use of instructional materials was measured by the number of times each instructional material was visited.

ProceduresStudents were asked to complete an autobiography at the beginning of the

semester explaining their background and experiences along with their expecta-tions for the course and their future goals and plans. The first unit introduced students to the course content and its objectives; it provided them with an opportunity to get to know one another and the instructor, and to establish a work routine for the course. In addition, the first unit introduced students to the content of the second unit through reading materials and initial discussion. Using a traditional design approach, the second unit built upon the content introduced in the first unit. Unit content and objectives were used to identify the best instructional strategies, which was used further to develop the instruc-tional materials and strategies. Given the units’ objectives, it was decided that a constructivist approach was a better design for the first two units. Thus, a prob-lem-based collaborative team activity along with student driven synchronous (live conference) and asynchronous discussions (forum) focused on a diverse set of readings were used as instructional strategies for the first two units. At the outset of the units, students were asked to read the required materials and par-


ticipate in synchronous and asynchronous discussions with the instructor and peers. They were also to begin working collaboratively with their team members to complete a problem-based team activity and submit their products. In order to form teams a sign up sheet was posted and students formed their own teams by choice. The instructor’s participation in the large group forum discussion (asynchronous) included posting opening remarks, asking questions if needed, offering more reading resources and finally summarizing the discussion at the conclusion of each unit. The instructor’s role during real time discussion was opening the session and facilitating students’ discussions and presentations and concluding the discussion by summarizing points made. At the end of the sec-ond unit, students completed a survey that assessed their perception and attitude.

Prior to any exposure to the content of the experimental units, students were asked to complete Felder and Soloman’s (1998) Index of Learning Styles Sur-vey (a self-scored survey) and report their results to the instructor. Students’ learning styles were then summarized and posted for their review. In addition, students were directed to review sites that provided information about differ-ent learning and thinking styles and to participate in a real time discussion about learning styles and how they might influence students’ learning. Students were also advised to write a reflection in their personal blogs about their own learning style and its educational value. Upon completion of the learning style survey, the instructor exposed students to readings and multiple instructional materials related to units’ content. The multiple instructional materials were designed to address students’ various learning styles using design framework described earlier (see Table 2). These materials consisted of instructional activi-ties, such as collaborative problem solving tasks, individual reflective blogs, asynchronous discussion board, and the schedule for synchronous discussions. Moreover, rather than facilitating student discussion during synchronous com-munication sessions, the real time sessions for the experimental design units were divided between instructor’s presentation and explanation of the material and student discussion, questions, and reflections. For the presentation part, the instructor used an interactive style, supplemented with multiple forms of presentations (visuals, verbal, polling items, demonstration). As with the tradi-tional design units, at the end of the fourth unit (experimental units), students completed a survey that assessed their perception and attitude toward the in-structional materials and strategies.

At the conclusion of the first two units with the traditional design approach, students produced a team product and participated in the large group discus-sion (asynchronous and synchronous) for each unit. Students’ learning products for the third and fourth units (experimental units) consisted of team products, individual reflective blogs, and postings in discussion forum. Students’ post-ings (minimum of four postings for each unit) in the large group asynchronous discussion board, and their team products and reflective blogs were scored by the instructor using evaluation rubrics. The scoring rubric evaluated team prod-ucts in terms of explicit evidence of using underlying concepts in the proposed solutions or design product, the quality of communicating the results, and the



degree of creativity in applying the concepts in practice. The scoring rubric for the discussion forum evaluated student postings in terms of using concepts from the assigned readings, contribution to the group discussion, and clear connection to student previous experiences or ideas presented in previous post-ings. The general assessment criteria that were used to develop the rubrics were described to students as part of the course syllabus and directions for the course assignments and their expectations. However, for both traditional and experi-mental units, students were informed that their performance products for the team activities and forum discussions were not numerically graded or counted directly in their final grades; rather, they were counted as part of the student active participation grade in the course. However, students received instructor’s detailed feedback and comments for team products and reflective blogs. This strategy was used to ease student anxiety for grades and improve their motiva-tion for learning.

Analysis,ResultsandDiscussionBoth qualitative and quantitative methods were used to analyze data. Analysis

of means and standard deviations, a test of correlation and cross tabulation were used to analyze perception or attitude survey and tracking data. Holistic judg-ment was used to score students’ blogs, postings in discussion boards and team products using separate scoring rubrics (Wiggins & McTighe, 2005). Students’ scores for each learning product were tabulated and analyzed using descriptive statistics. Paired-Sample T-test was used to compare students’ learning for tradi-tional and experimental design units across different measures.

Analysis of student autobiographies showed that 43% of students had from two to 10 years of teaching experiences, while 50% had four to 10 years of work experience in the areas of health education (14%), sales and management in business and industry (21%), and computer and media training (21%). One student had no previous work experience. The results of student learning styles survey are summarized in Table 3. As Table 3 shows, the majority of students were equally divided in different categories of learning styles with visual learners showing the highest frequency. Further analysis showed that all active learners (100%) were also visual learners, while only 50% of reflective learners were vi-sual. In addition, reflective and active learners were equally divided between se-quential and global learning styles, while 80% of active learners were intuitive, compared with 80% of reflective learners who were sensing learners. The results of student learning styles were also analyzed to determine whether there was any relationship between students’ work experiences and their preferred learning styles, as a number of researchers suspected (e.g., Kolb, Boyatzis & Mainemelis, 2000; Cuthbert, 2005). No correlation was found between categories of learn-ing styles and students’ number and type of previous work experiences. This result could be due to the small sample size (14 students) and majority female participants (Ford & Chen, 2001). However, a closer inspection of the data showed that students with teaching experiences tended to score balanced in the areas of reflective versus active and sensing versus intuitive.

Comparative analysis of student perception and attitude survey data for ex-perimental and traditional design units suggested some interesting results. One


of the survey items asked students to rate the following three types of interac-tions used in the course units (Moore, 1989) for their contributions to student learning: (a) student-content interaction, (b) student-instructor interaction, and (c) student-student interaction. Student-content interaction referred to students interacting with the course subject matter through readings or other instruc-tional materials available for each unit. Student-instructor interaction focused on any dialogue between students and instructor (e.g., mail, synchronous and asynchronous communications, or other cognitive guidance and feedback). Student-student interactions referred to interaction among individual students or among students working in small or large groups. When asked to rate con-tributions (four for very contributing, three for contributing, two for somewhat contributing and one for not contributing) of the three types of interactions to student learning, the mean scores for all three types of interactions were above 3.0 for both traditional and experimental units. However, students consistently rated all three areas of interactions lower for the experimental design units (see Table 4).

In order to further explore the relationship between students’ high ratings for the three types of interactions and their learning styles, the frequency of high ratings (“very contributing” and “contributing”) for the three types of interac-tions was calculated for the different types of learning styles (see Table 5, p. 230). The results indicated that students across all types of learning styles rated all three types of interactions lower for the experimental design, with student-

LearningStyles

%(#/14)

LearningStyles

%(#/14)

LearningStyles

%(#/14)

LearningStyles

%(#/14)

Reflective 36 (5) Intuitive 43 (6) visual 64 (9) Sequential 50 (7)Active 43 (6) Sensing 50 (7) verbal 29 (4) Global 50 (7)

Balanced 21 (3) Balanced 7 (1) Balanced 7 (1) Balanced 0

Table3:ResultsofStudentLearningStylesSurvey

N = 14

Survey Item Traditional DesignM (SD)

Experimental DesignM (SD)

Contributions of interaction between student and content to student learning

3.69(.48)

3.55(.52)

Contributions of interaction between the instructor and students to student learning

3.85(.38)

3.62(.51)

Contributions of interaction among students to student learning

3.77(.44)

3.08(.76)

Table4:DescriptiveResultsforSelectionofDifferentTypesofInteraction

N = 13 (One student did not complete all surveys)

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student interaction being the lowest. One explanation for this result is that when students did not have options, other than participating in a team activ-ity and discussion (student-student and student-instructor interaction), they tended to consider the interaction as a more or major contributing factor to their understanding. However, when students had a choice of selecting among different types of instructional materials and strategies (student-content interac-tion), some students (except for verbal learners) tended to change their minds. For example, reflective learners who had an opportunity to work individually and with learning materials no longer thought interaction among students con-tributed as highly to their learning. Similarly, visual learners who had access to visual instructional materials in addition to readings, as well as individual and team activities in experimental design units, did not find the interaction among students as a highly contributing factor to their learning. The lower rating for all three types of interactions for experimental design units may also be due to the novelty of the online course for the majority of students, and their assump-tion that they would have to work independently in an internet-based course. It is very likely that the novelty of having the opportunity to use different forms of interactions in an online course has worn off after the first two units of in-struction (traditional design) in which interaction, particularly among students and between student(s) and the instructor, was the core learning strategy and somewhat novel, given student perception of online courses. In addition, it is also possible that using Horizon Wimba—synchronous learning management tool—as a new technology for real time interaction with the instructor and other students, contributed to students’ high ratings for all three types of inter-actions during traditional design units. However, the novelty of the technology showed to be weakening after using it for two weeks during the traditional de-sign units.

LearningStyle

InteractionContent-student

InteractionInstructor-Students

InteractionStudent-Student

Tradition Design

%

Experimental Design

%

Tradition Design

%

Experimental Design

%

Tradition Design

%

Experimental Design

%

Reflective 83 60 80 66 50 16

Active 60 40 83 75 80 40

Intuitive 50 33 83 50 83 16

Sensing 83 66 83 66 66 50

visual 50 44 87 55 87.5 11

verbal 100 75 87 75 50 75

Sequential 67 33 83 50 83 17

Global 71 71 86 71 71 43

Table5:PercentageofStudentswithDifferentLearningStyleswhoRatedThreeTypesofInteractionas“veryContributing”

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Another survey item asked students to select from a list of instructional strat-egies; those strategies that were most contributing to student learning (e.g., team activity, synchronous/real time discussion, readings, online discussion, individual assignments, etc.). Collaborative team activity, readings, and online synchronous and asynchronous discussions were the only instructional strategies available in traditional design units, while individual reflective blog and self-assessment assignments and interactions with multiple instructional materials were added to the list of strategies for the experimental design units. Moreover, as indicated earlier, the role of the instructor and the nature of discussion dur-ing synchronous communication changed for the experimental design units. The results of the analysis for this survey item showed that 69% of students across all types of learning styles tended to select collaborative team activity as their first choice, followed by readings (46%) and forum or asynchronous discussion (23%) for traditional design units. For experimental design units, 31% of students across all types of learning styles listed instructor’s multiple in-structional materials and real time/synchronous discussion (31%), followed by collaborative team activity (23%) and readings (15%) as their first choices. As expected, students tended to differ more in their selection of preferred instruc-tional strategies for experimental units since they had more options available to them. Table 6 compares students’ selections of collaborative team activity as their first choice across different types of learning styles.

As Table 6 shows, more students across all types of learning styles tended to select collaborative team activity as their first choice for preferred instructional strategies, with active and visual learners showing the highest number for tradi-tional design units. However, for the experimental design units, this selection changed with a lower number of students across all types of learning styles selecting collaborative team activity as their first choice with sensing, visual, se-quential, and reflective learners showing the lowest numbers. Verbal learners’ se-

LearningStyle CollaborativeTeamActivities

TraditionDesign ExperimentalDesign

Active 80 40Reflective 50 16Intuitive 67 33Sensing 67 0Visual 75 11Verbal 50 50Sequential 67 14Global 71 29

N = 13 (One student did not complete all surveys)

Table6:Students’SelectionoftheInstructionalStrategiesContributedtoTheirLearning

�3� Winter�007-�008:Volume 40 Number 2Copyright © 2007, ISTE (International Society for Technology in Education), 800.336.5191


lection of collaborative team activity as their first choice remained the same for both traditional and experimental design units. Table 7 summarizes students’ selection of preferred instructional strategies for experimental design units. As Tables 7 shows, except for verbal learners, students tended to select instructional strategies that appeared to be more aligned with their learning styles.

Again, this outcome confirms that availability of various instructional strate-gies during experimental design units resulted in students’ use and selection of a variety of instructional strategies compared with traditional design. The results also suggest that students tended to select strategies that were more compatible with their learning styles. For instance, more visual learners tended to select multiple instructional materials compared with verbal learners who seem to prefer collaborative team activity, readings and real time discussions. However,

TypesofLearningStyles PreferredInstructionalStrategies(Frequency)

ReflectiveN = 5

• Real time/Synchronous Discussion (2)• Readings (2)• Multiple Instructional Materials (1)• Collaborative Team Activity (1)

ActiveN = 6

• Real time/Synchronous Discussion (3)• Collaborative Team Activity (2)• Multiple Instructional Materials (2)

VisualN = 9

• Multiple Instructional Materials (4)• Real time/Synchronous Discussion (4)• Collaborative Team Activity (1)• Readings (1)

VerbalN = 4

• Collaborative Team Activity (2)• Readings (1)• Real time/Synchronous Discussion (1)

SensingN = 7

• Real time/Synchronous Discussion (3)• Readings (2)• Multiple Instructional Materials (1)

IntuitiveN = 6

• Multiple Instructional Materials (3)• Collaborative Team Activity (2)• Real time/synchronous Discussion (1)

SequentialN = 7

• Real time/Synchronous Discussion (3)• Multiple Instructional Materials (2)• Collaborative Team Activity (1)• Readings (1)

GlobalN = 7

• Real time/Synchronous Discussion (3)• Readings (2)• Multiple Instructional Materials (1)• Collaborative Team Activity (1)

Table7:Students’SelectionofPreferredInstructionalStrategiesforExperimentalDesignUnits

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for the experimental design units, the following four multiple instructional strategies seemed to be perceived as more contributing to student learning: Multiple instructional materials (designed to address different learning styles), synchronous or real time discussion, collaborative team activity, and readings. As indicated earlier, students selected collaborative team activity, readings, and asynchronous discussion as their first choice for the traditional design units. This result indicates that while some strategies appear to be useful to all stu-dents with any learning styles (e.g., readings, problem solving, collaborative work with peers, real time discussion), students tend to become more selective if other strategies are readily available.

Analysis of the tracking system (see Table 8) showed that the number and period of time that students read or viewed postings in the forum discussion or used chat rooms to communicate with each other were reduced during ex-perimental design units. However, the number and the period of time that students read or viewed assignments (collaborative team activity, reflective blog) and content related pages (lessons’ objectives, readings, and instructional materials) increased for experimental design units. This result is not surprising because team activity and forum discussions were the only assignment related pages during traditional design units. Furthermore, while collaborative team activity, forum, and real time discussions were the core learning activities during traditional design units, individual reflective assignments and self-assessment quizzes combined with instructional materials in multiple formats were added to the list of learning activities for experimental design units. Thus, students had to spend more time browsing and reading assignments, assessments, and content related materials rather than focusing on discussion in chat rooms and discussion boards.

In order to examine whether there was any relationship between learning styles and the number of pages viewed, an analysis was conducted. The results indicated that reflective learners (83%) spent less time viewing/reading postings in the discussion board during experimental design units, compared to active learners (60%). In addition, both reflective and active learners spent more time

Tool Traditional Units Total Time Experimental units Total TimeAssignments 424 17:44:57 744 57:38:01Chat 189 10:59:34 74 14:48:01Content File 854 48:40:54 1152 47:11:44Discussion 956 147:42:54 716 78:43:17File Manager 87 7:07:35 153 11:25:43Mail 718 59:51:50 834 72:33:01Media Library 252 19:30:11 269 39:40:05Organizer 15 0:53:53 1286 10:14:48

Table8:numberandPeriodofTimeThatStudentsReadorviewedCourseContentandMaterials



viewing assignment pages during experimental design units, although they dif-fered in viewing content related materials. Eighty three percent (83%) of reflec-tive learners (20 to 99 times) spent more time viewing content related materials, compared with 60% of active learners (13 to 73 times) during experimental design units. Furthermore, while both visual and verbal learners spent more time viewing content related materials, visual learners (78%) spent consider-ably more time viewing content related pages (40 to 99 times more) during experimental design units. These findings suggest that learning styles may play a role in viewing pages when there is an option. However, since in this study students had to participate in all learning activities (matched and mismatched), flexibility was only available for ways in which students wanted to approach the learning tasks and for number and the periods of time they wanted to spend in exploring instructional materials. In addition, the increase in the use of matched instructional materials by participants might be due to the attempt in the experimental design to improve student knowledge of their own learning styles and to inform them of the instructor’s effort to address their differences in the design of the experimental units. Although researchers do not seem to agree that student knowledge of his/her own learning style is likely to make much difference in his/her learning, they agree that if such knowledge is used by the instructor to encourage the learner to consider the nature of learning, under-standing, and how he/she personally deals with the process, an impact may ap-pear (e.g., Cuthbert, 2005; Jones, 1993; Perry, 1994).

Two open-ended survey items measured students’ attitude and satisfaction for the units. One item asked students to explain their learning experiences for the units, and the other asked students to suggest changes to improve the learning experiences for the units. Students’ responses at the end of traditional design units indicated that they had very positive learning experiences (e.g., “Very good. A solid start,” “I would describe my learning experiences as being surprisingly effective,” “I would describe my learning experience as very posi-tive”). Students found readings interesting and considered collaborative team activity and real time discussion very effective strategies for learning (e.g., “The group discussions and the collaborative team activity helped to cement the material that was covered,” “I am amazed at how much I can learn in an online class. I already feel like I have learned a lot,” “I honestly feel that the real-time discussion and group project really cemented the concepts into my brain”). Students’ suggestions for improvement can be summarized into four categories: combine the forum discussion for the two units, limit the length of the post-ings in the forum, spend more time on team activity, and add some individual assignments. The majority of reflective learners wanted to spend less time read-ing postings in the forum and responding, while the majority of active learners wished that they had more time to work with their team members on the team activity.

Students’ responses to the attitude survey items at the end of the experimental design units indicated that they thought their learning experience was meaning-ful and they were challenged by the content of the units and learning activi-ties. However, students appeared to be more divergent in their explanation of


what they considered as positive learning experiences (e.g., I have found the information to be very challenging and complex,” “Very much more in depth. Understanding is becoming easier,” “I enjoyed the Merrill video as well as the interactive tutorials,” “I felt that my learning experience was very meaning-ful,” “Much more focused on constructing meaning rather than cramming in reading”). In addition, students did not suggest any major changes for the ex-perimental design units and thought that the units were well designed (“I don’t think I’d change anything at this point. Everything seems to be helping me,” “I was happy with the design. I don’t think I would change anything,” “I would not change the format”).

Descriptive analysis of student products of learning (team product, postings in the discussion board, individual reflective blogs) for traditional and experi-mental design units showed the following results. Table 9 summarizes the mean scores for team products completed during traditional and experimental design units. As Table 9 shows, students’ mean scores were higher for both team prod-ucts during the traditional design units, therefore, significantly different from experimental units (t = 3.41(df = 13); p=.005). Closer inspection of the data showed that the low mean score for the second activity during the experimental design was due to an extreme low score (an outlier) for one team’s product. The team’s low score was due to an escalated conflict among team members, which was reported to the instructor but it could not be resolved before the team submitted its products. Removing the outlier team from the data, however, did not affect the results and student performance indicated that students achieved higher scores during traditional design units (t = 3.42 (df =10); p = .007), al-though the mean scores for the team products during experimental design were higher after removing the outliers.

The mean scores for student postings in the discussion board during tradi-tional (M = 34.2) and experimental design (M = 35.5) showed no statistically

TeamProducts Mean n Std.Deviation Std.ErrorMean

Traditional Design

Team Activity 1 97.1429 14 4.68807 1.25294Team Activity 2 95.7143 14 5.83660 1.55990

Experimental Design


TeamProducts(afterremovingtheoutlier) Mean n Std.Deviation Std.Error

Mean

Traditional Design


Experimental Design


Table9:TheMeanScoresforTeamProductsCompletedDuringTraditionalandExperimentalDesignUnits.



significant difference (t =-.349 (df =13); p = .733). Analysis of students’ scores for their postings in the discussion board across different types of learning styles, however, pointed to some differences. While 80% of active learners scored higher for their postings during experimental design units (see Table 10) only 50% of reflective learners showed this pattern of change. In addition, ac-tive learners scored higher for their postings in both traditional and experimen-tal design units compared with reflective learners. Active learners’ higher per-formance in discussion boards may not be a surprise since according to learning style theory, active learners tend to be more compatible with instructional strategies that provide an opportunity for group discussion and sorting of ideas. Similar results were shown in previous studies conducted by Felder, Felder, and Diatz (2002), McCaulley, Godleski, Yokomoto, Harrisberger, and Sloan (1983) and Rosati (1997).

Two activities could explain the improvement in active learners’ performance for experimental design units. First, active learners had an opportunity to com-plete individual blogs. This activity allowed students to reflect on reading mate-rials while participating in asynchronous discussion (a mismatched strategy for their learning style). In addition, active learners, who were also visual in their style, had access to multiple instructional materials, and could select the ma-terials that were matched with their learning styles. Visual and verbal learners’ scores, as well as sequential and global and intuitive and sensing learners’ scores, did not show any major changes across traditional and experimental design units.

LearningStyles

Students TraditionalDesign ExperimentalDesign

DiscussionPostingsMean

DiscussionPostingsMean

Reflective 1 49.5 33.52 27 28.53 30.5 57.54 47 355 23 276 19 7.5

AverageScore 32.75 31.5Active 1 43.5 52.5

2 28 41.53 38 514 33.5 265 35 45

AverageScore 35.6 43.2

Table10:ReflectiveandActiveStudents’AverageScoresforPostingsintheDiscussionBoards


Analysis of student performance for individual reflective blogs during experi-mental design units pointed to some differences among students with different learning styles. Reflective and verbal learners scored higher compared with ac-tive and visual learners (see Table 11), although the difference was not signifi-cant. This result was confirmed by visual and active learners’ comments that they did not like writing blogs and found it to be a difficult task. Given char-acteristics of reflective and verbal learners, this result is expected and consistent with learning styles theories and research (e.g., Felder, Felder, & Diatz, 2002; Kolb, Boyatzis, & Mainemeli, 2000).

LimitationsoftheStudyThe present study has certain limitations that need to be taken into account

when considering the study and its contributions. However, some of these limi-tations can be seen as fruitful avenues for future research under the same theme.

The selection of the single case design naturally brings forth limitations as far as the generalization of the results of the study is concerned. The sample size was particularly small and the participants were not representative of a broad population of graduate students. Furthermore, short exposure durations for both traditional and experimental design units and exclusive reliance on two units of instruction could have influenced the results. Given the controversy in the literature surrounding this issue, future studies should consider cross sec-tional design, longer exposure to instruction and instructional materials, larger and more diverse sample size, and various subject matters.

COnCLUSIOnSAnDIMPLICATIOnSDesigning and developing instructional materials that address multiple learn-

ing styles and employing various instructional strategies for online learning

Style Students Blogs(M)

Styles Blogs(M)

Style Blogs(M)

Style Blogs(M)

ReflectiveN = 6

1 100 ActiveN = 5

100 visualN = 9

93 verbalN = 4

1002 100 83 100 1003 70 60 70 1004 96.5 53.5 93.5 875 100 80 96.5

6 87 83

7 60

8 53.5

9 80.0

AverageScore 92.3 75.3 81.1 96.75

Table11:Students’AverageScoresonIndividualBlogsAcrossDifferentTypesofLearningStyles



environments are time consuming and require careful design, development, im-plementation and evaluation of instruction. The results of this study shed light on whether or not such an attempt is worthy of time and effort. Furthermore, while many studies show that matching student learning styles with instruc-tional strategies improves learning, some instructional designers and educators argue that content and expected outcomes of learning must decide what strate-gies should be used to deliver instruction, rather than matching instruction to individual learning styles (Merrill, 2000).

The results of this study and the model that was used to design instruction for multiple learning styles provide evidence that factors such as learning tasks and the context in which learning is to transpire (e.g., student level of engage-ment in the learning process, level of interactivity among students and between student and the instructor, and instructor immediacy behavior) and student’s perception of social presence (social and human qualities of online learning) (Hackman & Walker, 1990) may play a more significant role in improving in-structional effectiveness in online learning. The comparison between traditional and experimental design units pointed to a significant difference in student learning in favor of a traditional design approach, but it did not show a major difference in student satisfactions. In addition, some changes in student inter-action with various self-instructional materials (student-content interaction) and students’ perception of their depth of learning were observed. It appeared that multiple forms of interactivity, including synchronous virtual classroom discussion during traditional design units, combined with student engagement in collaborative problem solving activities, helped students adjust their strategic approaches to learning in order to achieve expected learning outcomes without impacting their attitude and satisfaction. In other words, it seems that in online learning environments where social interaction, collaboration and problem solv-ing are highly emphasized, it is likely that students’ perception of their positive learning experience influence their motivation and willingness to adjust their preferred learning styles. This result has been supported by researchers who have studied teaching and learning in online learning environments (e.g., Gar-rison, Anderson, & Archer, 2000; Wenger, 2001). For example, after reviewing literature on interactions in online learning, Anderson (2002) postulates that “sufficient levels of deep and meaningful learning can be developed as long as one of the three forms of interaction (student-teacher; student-student; stu-dent-content) are at very high levels” (n.p.). In addition, it is also likely that a collaborative problem solving activity, as a learning task, is not an antithetical strategy that causes learners with different learning styles to feel too uncomfort-able to learn effectively. Rather, it is possible that the problem solving strategy creates an open learning environment in which students are able to employ a full range of learning skills and strategies (Smith & Renzulli, 1984). Finally, the insignificant difference in some performance results may also suggest that stu-dent perception of social presence (quality of human interaction) might have as large an impact on students’ motivation for learning and their level of effort and engagement in traditional design units as offering various instructional strate-gies for different learning styles. However, it should be noted that the above-


mentioned results in this study could have been influenced by participants’ backgrounds and experiences, as well as the course subject matter. In addition, since participants in this study only were exposed to traditional design units for two weeks, it is not clear if their attitude and satisfaction would have stayed positive if all course units were designed using the traditional approach. Future studies should focus on diverse groups of learners and various subject matters. Teaching two courses with similar content and learner populations should be used as experimental and control groups in order to be able to generalize the above mentioned results.

In spite of the significant difference in student learning in favor of tradi-tional design units, the results of this study suggest that learning styles can be integrated into instruction in online learning environments without compro-mising the appropriateness of instructional strategies for specific content and learning outcomes. The match and mismatch strategy combined with Kolb’s (1984) Learning Cycle model employed in the design and organization of the experimental units appears to provide enough flexibility and learner control, yet opportunities for students to learn in a most effective manner. The point of using learning styles in the design of experimental units in this study was not to determine each student’s preferred instructional approach (visual or verbal, sensing or intuitive, etc.) nor to provide instruction exclusively in that manner. Rather, it was designed to (1) address students’ preferences by providing enough control and flexibility in the ways they are more comfortable to learn, and (2) require students to function in their less preferred approach by allowing them to begin their learning cycle in the manner that they preferred. The results sug-gest that even though measures of student learning do not point to any major differences, there are some qualitative differences in student approaches to learning when learning styles are considered. For instance, while writing reflec-tive journals and interacting with readings and other instructional materials were preferred learning strategies for reflective learners, active learners seemed to benefit from engaging in such activities. On the other hand, while collab-orative work and group discussion were not favored by reflective learners, they seemed to agree that these activities contributed to their learning. Furthermore, integrating learning styles in the design of instructional materials seemed to en-courage learners to spend more time interacting with the course content and ex-ploring various instructional materials to achieve learning outcomes. Although social presence is often set in synchronous or asynchronous activities in online learning, it can also be set through audio-, video-, or text-based presentations. Direct instruction can also be provided through an instructor’s annotations of the scholarly work of others and notes. If addressing multiple learning styles through a variety of instructional strategies could result in improving student interaction with the course content, it may very well result in higher learning outcomes, as participants in this study commented. The future research should use measures of learning outcomes that assess students’ deep understanding of underlying concepts, in addition to the applications of these concepts in prac-tice, as used in this study. Such measures might better discriminate between deep versus shallow concept learning that is likely to result from being exposed to a full range of interactions during experimental design.



The match/mismatch strategy used in this study required students to expand their learning preferences by engaging in different learning activities. Whether or not this strategy helped learners modify their learning styles and become more balanced learners was not examined in this study. Long term studies are needed to determine whether balanced instructional strategies help learners ex-pand their learning styles. The instructional effectiveness of applying the design model used in this study will be reinforced if such results are confirmed by stud-ies with a larger sample size. Many learning styles theorists argue that today’s increasingly more complex and service-oriented jobs demand flexibility in learn-ing approaches for success (Kolb, Boyatzis, & Mainemeli, 2000). Therefore, more studies are needed to show whether or not people with balanced learning profiles are more sophisticated or adaptively flexible learners.

Finally, this study confirms that integrating learning styles into the design of instruction in online learning is possible and will change significantly the design role of online instructors from one of content creation to one of customization, application, and contextualization of learning sequences (Koper, 2001). The findings also suggest that integrating student preferences for learning helps the course designers to improve interactivity and social presence in online learning environments.

ContributorMahnaz Moallem is a professor of instructional technology and research and

program coordinator for the Instructional Technology Graduate program at the University of North Carolina, Wilmington, Watson School of Education. She is currently on a two-year reassignment at the National Science Foundation, serv-ing as an instructional technology project leader. (Address: Watson School of Education, Dept. Instructional Technology, Foundations & Secondary Educa-tion, 601 South College Road, Wilmington, NC 28403; Phone: 910.962.4183; Fax: 910.962.3609; [email protected])

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